Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells

项目总结 用仿生细胞皮坦模拟肿瘤信号级联的异质性 开发捕捉癌症信号级联异质性的仿生系统将增强 开发有效的抗癌治疗方法，降低候选药物的流失率。一种癌症- 信号级联由受体组成，这些受体将信号传播到蛋白质网络，然后蛋白质网络进行调制 基因网络的表达谱。在癌症中，信号级联可以表现出巨大的异质性 对其蛋白质成分的浓度、组成和序列的变化。这种异质性具有 已知会降低某些抗癌药物的疗效。然而，到目前为止，普遍缺乏 模仿癌症信号异质性的工程系统直接级联。即使细胞 培养和异种移植捕获了肿瘤的物理结构，但它们不能直接控制 有针对性的信号级联。在这里，我们建议通过设计一种仿生细胞来克服这一瓶颈 一种重组癌症信号级联的变体以模仿其异质性的方法。每种仿生生物 细胞是一个合成系统，它将通过模仿细胞膜和一个自下而上的结构来构建 一个异质癌症信号级联的实例。作为概念的证明，我们将调查建议的 使用由血小板衍生生长因子受体β(PDGFRβ)激活的RAS信号级联的想法。 该方案包括两个主要步骤：1)在仿生体内重建核心的PDGFRβ信号级联 细胞。2)仿生细胞内PDGFRβ信号级联的模型异质性。我们将构建一个 使用生物打印机的仿生单元库，每个单元库包含一个唯一的信号级联实例 这将在明确的组成和浓度下混合信号级联的每一种蛋白质成分。 仿生细胞库和计算模型将被整合，以研究抗癌的稳健性 抑制异质信号级联反应的药物。提出的想法挑战了 通过重组(部分或全部)至少100,000个独特的和 仿生细胞内癌症信号级联的生理相关实例，这将是 用于高通量药物筛选的小型化。拟议的工作意义重大，因为它使多个 针对靶向癌症信号级联的明确异质性的药物的维度筛选。如果 如果成功，这项研究将为研究异质性的影响提供一种有效的、可推广的方法 肿瘤通路对抗癌药物疗效的影响。

PROJECT SUMMARY Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells, PI Tan Developing biomimetic systems that capture the heterogeneity of cancer-signaling cascades will enhance the development of effective anti-cancer therapy and reduce the attrition rate of drug candidates. A cancer- signaling cascade consists of receptors that propagate signals to protein networks, which then modulate expression profiles of gene networks. A signaling cascade can exhibit tremendous heterogeneity in cancers due to variation in the concentration, composition, and sequence of its protein constituents. Such heterogeneity has been known to diminish the efficacy of certain anti-cancer drugs. To date, however, there is a general lack of engineered systems that emulate the heterogeneity of cancer-signaling cascades directly. Even though cell cultures and xenografts capture physical structure of tumors, they do not directly control the heterogeneity of a targeted signaling cascade. Here, we propose to overcome the bottleneck by engineering a biomimetic-cell approach that reconstitutes variants of a cancer-signaling cascade to mimic its heterogeneity. Each biomimetic cell is a synthetic system that will be constructed from the bottom-up by mimicking cell membranes and one instance of a heterogeneous cancer-signaling cascade. As a proof of concept, we will investigate the proposed idea using the Ras signaling cascade activated by the platelet-derived-growth-factor receptor beta (PDGFRβ). The proposal consists of two main steps 1) Reconstitute the core PDGFRβ signaling cascade inside biomimetic cells. 2) Model heterogeneity of the PDGFRβ signaling cascade inside biomimetic cells. We will construct a library of biomimetic cells, each containing one unique instance of the signaling cascade, using a bio-printer that will mix each protein constituents of the signaling cascade at well-defined composition and concentrations. The biomimetic-cell library and a computational model will be integrated to study robustness of anti-cancer drugs in inhibiting the heterogeneous signaling cascade. The proposed idea challenges the main paradigm in studies of cancer-signaling cascades by reconstituting (partially or fully) at least 100,000 unique and physiologically relevant instances of a cancer-signaling cascade inside biomimetic cells, which will be miniaturized for high-throughput drug screening. The proposed work is significant because it enables multi- dimensional screening of drugs against well-defined heterogeneity of the targeted cancer-signaling cascade. If successful, this study will yield a validated, generalizable approach for studying the impact of heterogeneous cancer pathways on the efficacy of anti-cancer drugs.